Stock selection involves the continuous quest for the margin of safety, or a favorable difference between the stock price and its intrinsic value. Although this variable might not be quantified with exact precision, it may be approximated through the underlying relationships in financial markets and the real economy. We propose Genetic Network Programming with changing structures(GNP-cs), a novel evolutionary based algorithm to approximate these relationships through graph networks, and build asset selection models to identify the prospective stocks in the context of changing environments. GNP-cs uses functionally distributed systems to monitor the change of the economic environment and execute the strategy for stock selection adaptively. The comparison shows that the proposed scheme outperforms the standard stock selection styles using the stocks listed in the Russell 3000 Index. This paper suggests that the use of evolutionary computing techniques is an excellent tool to tackle the stock selection problem, whose advantages imply the usefulness to manage the risk and safeguard investments.